This article was published in the journal Control Engineering Practice [© Elsevier]. The definitive version is available at: http://dx.doi.org/10.1016/j.conengprac.2011.10.015Small-scale helicopters are very attractive for a wide range of civilian and military applications due to their unique features. However, the autonomous flight for small helicopters is quite challenging because they are naturally unstable, have strong nonlinearities and couplings, and are very susceptible to wind and small structural variations.\ud \ud A nonlinear optimal control scheme is proposed to address these issues. It consists of a nonlinear model predictive controller (MPC) and a nonlinear disturbance observer. First, an analytical solution of the MPC is developed based on the nominal model under the assumption that all disturbances are measurable. Then, a nonlinear disturbance observer is designed to estimate the influence of the external force/torque introduced by wind turbulences, unmodelled dynamics and variations of the helicopter dynamics. The global asymptotic stability of the composite controller has been established through stability analysis. Flight tests including hovering under wind gust and performing very challenging pirouette have been carried out to demonstrate the performance of the proposed control scheme

Agriculture is facing severe challenges from crop stresses, threatening its sustainable development and food security. This work exploits aerial visual perception for yellow rust disease monitoring, which seamlessly integrates state-of-the-art techniques and algorithms including UAV sensing, multispectral imaging, vegetation segmentation and deep learning U-Net. A field experiment is designed by infecting winter wheat with yellow rust inoculum, on top of which multispectral aerial images are captured by DJI Matrice 100 equipped with RedEdge camera. After image calibration and stitching, multispectral orthomosaic is labelled for system evaluation by inspecting high-resolution RGB images taken by Parrot Anafi Drone. The merits of the developed framework drawing spectral-spatial information concurrently are demonstrated by showing improved performance over purely spectral based classifier by the classical random forest algorithm. Moreover, various network input band combinations are tested including three RGB bands and five selected spectral vegetation indices by Sequential Forward Selection strategy of Wrapper algorithm.

Finding the location and strength of an unknown hazardous release is of paramount importance in emergency response and environmental monitoring; thus, it has been an active research area for several years known as source term estimation (STE). This paper presents a joint Bayesian estimation and planning algorithm to guide a mobile robot to collect informative measurements, allowing the source parameters to be estimated quickly and accurately. The estimation is performed recursively using Bayes' theorem, where uncertainties in the meteorological and dispersion parameters are considered and the intermittent readings from a low-cost gas sensor are addressed by a novel likelihood function. The planning strategy is designed to maximize the expected utility function based on the estimated information gain of the source parameters. Subsequently, this paper presents the first experimental result of such a system in turbulent, diffusive conditions, in which a ground robot equipped with the low-cost gas sensor responds to the hazardous source simulated by incense sticks. The experimental results demonstrate the effectiveness of the proposed estimation and search algorithm for STE based on the mobile robot and the low-cost sensor.

This paper studies the tracking control problem of an aerial manipulator that consists of a quadcopter flying base and a Delta robotic arm. We propose a novel control approach that consists of extended state observers (ESOs) for dynamic coupling estimation, ESO-based flight controllers, and a cooperative trajectory planner. Compared to the state-of-the-art approaches, the proposed one has some attractive features. First, it requires much less measurement information as opposed to the full-body control approaches and hence can be implemented conveniently and efficiently in practice. Second, while the existing approaches estimate the coupling effect based on precise models, the proposed ESOs can do that based on much less information about the system model. The proposed approach is verified by four experiments on a real aerial manipulation platform. The experimental results show that the average tracking error can reach 1 cm by the proposed approach as opposed to 10 cm by the PX4 baseline controller. Although force control is not considered specifically in the approach, the system can complete aerial weaving tasks thanks to the ESOs in the presence of drag forces applied to the end-effector during manipulation. Note to Practitioners —Aerial manipulators have received increasing research attention in recent years due to their wide range of applications. In this paper, we particularly focus on the high-precision and robust control of aerial manipulators. We propose a novel control approach that consists of extended state observers (ESOs) for dynamic coupling estimation, ESO-based flight controllers, and a cooperative trajectory planner. Four experiments on a real aerial manipulation platform demonstrate the effectiveness of the approach. In future research, we will address the control problem when the aerial manipulator contacts the environment.

This article presents a novel path-following algorithm for fixed-wing unmanned aerial vehicles by virtue of a nonlinear optimal control approach and wind disturbance observers. Different from some exiting algorithms, the proposed algorithm formulates the path-following problem into a control problem by introducing auxiliary dynamics for the path parameter. The proposed controller is designed in an optimal and systematic manner where the control action is generated according to a well-defined cost function. This framework does not require any complex geometric coordinate transformation and can be easily tuned to accommodate curved reference paths, making it straightforward to deploy in different flight missions. Moreover, the wind influences on the path-following performance is explicitly compensated by the proposed algorithm based on the wind estimates provided by nonlinear disturbance observers. The closed-loop stability, including the auxiliary dynamics for path parameter and observer dynamics for wind estimation, is also analyzed. The feasibility and effectiveness of the proposed algorithm have been thoroughly validated in simulation studies and realistic flight tests.

In this paper, a new method for planning coverage paths for fixed-wing Unmanned Aerial Vehicle (UAV) aerial surveys is proposed. Instead of the more generic coverage path planning techniques presented in previous literature, this method specifically concentrates on decreasing flight time of fixed-wing aircraft surveys. This is achieved threefold: by the addition of wind to the survey flight time model, accounting for the fact fixed-wing aircraft are not constrained to flight within the polygon of the region of interest, and an intelligent method for decomposing the region into convex polygons conducive to quick flight times. It is shown that wind can make a huge difference to survey time, and that flying perpendicular can confer a flight time advantage. Small UAVs, which have very slow airspeeds, can very easily be flying in wind, which is 50% of their airspeed. This is why the technique is shown to be so effective, due to the fact that ignoring wind for small, slow, fixed-wing aircraft is a considerable oversight. Comparing this method to previous techniques using a Monte Carlo simulation on randomised polygons shows a significant reduction in flight time.